How to summarize documents with AI on AWS (2026).
Turning long documents — PDFs, contracts, transcripts, research, whole corpora — into faithful summaries is one of the highest-value, lowest-risk things to build on Bedrock. This is the full how-to: the end-to-end pipeline (parse → chunk → summarize → assemble → evaluate), the central decision between map-reduce chunking and single-pass long-context, how to choose a model (and why the cheap tiers win at scale), how to summarize a whole corpus cheaply with batch inference, the prompt patterns that actually produce faithful summaries, how to measure faithfulness, and what production really costs.
- Document summarization on AWS is a pipeline, not a single API call: parse the document to clean text (Amazon Bedrock Data Automation or Amazon Textract), chunk it if it is long, summarize, assemble the pieces into one coherent summary, then evaluate that the summary is faithful to the source. The parsing and chunking decisions drive quality more than the model choice does.
- The central decision is map-reduce vs long-context single-pass. If the document fits comfortably in the model context window, summarize it in one call (simplest, most coherent). If it is longer than the window — or you want tighter cost control — use map-reduce: summarize each chunk, then summarize the summaries. Map-reduce scales to any length; single-pass is simpler and more coherent when it fits.
- Cost is dominated by input tokens, so two levers matter most: pick the cheapest model tier that clears the quality bar (Amazon Nova Lite or Micro, a Claude Haiku model; summarization rarely needs a frontier model), and run bulk or corpus summarization on Bedrock batch inference, which AWS prices 50% lower than on-demand for select models.
IWhat document summarization on AWS actually involves
Summarizing a paragraph is one model call. Summarizing real documents — multi-page PDFs, scanned contracts, hour-long transcripts, or a corpus of thousands of files — is a pipeline, because two problems sit in front of the model before it ever sees clean prose: the document is not text yet, and it may not fit in the context window.
It is tempting to think of summarization as "send the document to a model, get a summary back." That works for a short, clean, plain-text input. It breaks the moment the input is a real-world document, for two concrete reasons. First, most documents are not clean text. A PDF is a layout format, not a text format; a contract may be a scan; a transcript arrives with timestamps and speaker tags; a slide deck is mostly images. Before any model can summarize a document, something has to extract clean, ordered text from it — and bad extraction (broken tables, headers fused into body text, two columns interleaved) silently poisons every summary downstream.
Second, real documents are often longer than the model's context window — or long enough that putting the whole thing in one prompt is wasteful even when it fits. A 200-page contract, a year of meeting transcripts, or a 10-K filing cannot always go in a single call, and even when a long-context model could swallow it, you may not want to pay to push 300,000 tokens through a frontier model. This is why the central design decision in summarization is how you decompose a long document into model-sized pieces and reassemble the result.
So a production summarization system on AWS is really five logical stages: parse the source into clean text, chunk it if it exceeds your chosen window, summarize each piece (or the whole thing), assemble the pieces into one coherent summary, and evaluate that the summary is faithful to the source rather than confidently wrong. Every stage maps to a managed AWS service, which is what makes AWS a natural place to build this.
One framing worth keeping throughout: summarization is a high-value, low-risk GenAI use case. Unlike open-ended generation, the output is constrained by a source you can check it against, so faithfulness is measurable and hallucination is controllable. That, plus the fact that the cheap model tiers are usually good enough, is why summarization is often the first GenAI workload a team ships — and why it is a natural fit for a funded proof-of-concept.
Document summarization on AWS = parse the document to clean text (Bedrock Data Automation / Textract) → chunk it if it is longer than your context window → summarize (single-pass or map-reduce) → assemble one coherent summary → evaluate faithfulness. Parsing and chunking decide quality; model choice and batch decide cost.
IIThe reference summarization pipeline on AWS, stage by stage
Every summarization system — a single PDF or a corpus of millions — runs the same five logical stages. Knowing each one is what lets you debug a system that returns vague or wrong summaries, because nearly every quality problem traces back to a specific stage.
It helps to see the whole shape first. Stages 1–2 (parse, chunk) are document preparation; stage 3 (summarize) is the model work; stages 4–5 (assemble, evaluate) turn raw model output into something you trust. The table at the end of this section maps each stage to the AWS service that typically implements it.
1. Parse — get clean, ordered text out of the document
The job here is to turn whatever the source is — PDF, scan, image, Office file, HTML, audio transcript — into clean, correctly-ordered plain text (plus useful structure like headings and tables). On AWS there are two main tools. Amazon Textract is the OCR/document-analysis service: it extracts text, forms, and tables from scanned or image-based documents and is the right tool when you have scans, photos of documents, or forms. Amazon Bedrock Data Automation is the newer, higher-level option: it ingests documents (and images, audio, and video) and returns clean, structured, GenAI-ready output — extracted text, layout, and field-level data — in one managed step, which removes most of the custom parsing glue teams used to write. For born-digital text (clean PDFs, Markdown, HTML) a lightweight text extractor is often enough.
This stage is the most under-rated. Garbage in, garbage out applies with full force: if a two-column page is read straight across, if a table collapses into a wall of numbers, or if page headers and footers get mixed into the body, the model will faithfully summarize the mess. Spend the effort here — it pays off at every later stage.
2. Chunk — split the document if it exceeds your window
If the cleaned text fits comfortably in the context window of the model you have chosen, skip this stage and summarize in one pass. If it does not — or if you have decided to control cost with map-reduce regardless — split the document into pieces small enough to summarize well. Unlike RAG, where chunks are tuned for retrieval precision, summarization chunks should be as large as the model handles comfortably while staying coherent, and should respect document structure: split on section and chapter boundaries, keep tables and clauses intact, and carry a little overlap so a thought split across a boundary survives. Structure-aware splitting (on headings, sections, or natural breaks) beats blind fixed-size splitting for summaries, because each chunk then corresponds to a coherent unit you can summarize on its own.
3. Summarize — the model call(s)
This is where a foundation model on Amazon Bedrock does the work. For a document that fits, it is a single call with a good summarization prompt (section IV). For a chunked document, it is one call per chunk in the "map" step. The model is chosen for cost-per-quality, not raw capability — summarization is one of the easiest tasks for modern models, so a small, fast tier (Amazon Nova Lite/Micro, Claude Haiku, a small Llama/Mistral) is usually the right answer, especially at volume. Section III covers the strategy and section V the model economics.
4. Assemble — combine pieces into one coherent summary
For single-pass summarization this stage is trivial — there is one summary. For map-reduce it is the "reduce" step: take the per-chunk summaries and summarize them into one final summary, optionally in several rounds if there are many chunks. Done naively, reduce produces a disjointed list of section summaries; done well, it produces a single coherent narrative. The reduce prompt matters — it should ask the model to synthesize and de-duplicate across the chunk summaries, not merely concatenate them, and to preserve the overall structure (executive summary, key points, decisions/risks) you want in the output.
5. Evaluate — check the summary is faithful to the source
A summary that reads beautifully but invents a clause, misstates a number, or drops the one risk that mattered is worse than no summary. The final stage measures whether the summary is faithful (every claim is supported by the source), complete (it captures the key points), and relevant (it answers what the summary is for). Amazon Bedrock's model-evaluation suite can run an LLM-as-a-judge to score summary quality automatically; section VI covers how. This stage is what separates a demo from something a business will trust.
| Stage | Phase | What it does | Typical AWS service |
|---|---|---|---|
| 1. Parse | Document prep | Source → clean, ordered text | Bedrock Data Automation / Amazon Textract |
| 2. Chunk | Document prep | Split long docs into model-sized pieces | Lambda / Glue / Step Functions (DIY) |
| 3. Summarize | Model work | Summarize each piece (or whole doc) | Claude / Nova / Llama / Mistral (Bedrock) |
| 4. Assemble | Synthesis | Reduce piece summaries into one | Bedrock model call (the "reduce" step) |
| 5. Evaluate | Quality gate | Score faithfulness + completeness | Bedrock model evaluation (LLM-as-a-judge) |
IIIMap-reduce vs long-context single-pass — the core choice
The decision that shapes a summarization system is not which model — it is how you handle length. Either the whole document goes to the model in one call (long-context single-pass) or you decompose it, summarize the pieces, and summarize the summaries (map-reduce). Everything else follows from this choice.
The honest framing: if it fits, do single-pass; when it does not fit or cost matters, do map-reduce. Modern long-context models (Claude on Bedrock and others now offer very large context windows) can swallow surprisingly large documents in one call, and when they can, single-pass is simpler and produces a more coherent summary because the model sees everything at once. Map-reduce exists for the documents that genuinely exceed the window — and as a cost-control tool even for documents that would fit.
Single-pass (stuff the whole document into one call)
You parse the document, put the entire cleaned text into one prompt with a summarization instruction, and get one summary back. Pros: dead simple to build; the most coherent output because the model has full global context and can weigh the whole document; no assemble step. Cons: capped by the context window (it physically cannot handle a document larger than the window); pushing a very large document through a capable model on every request can be expensive; and quality can soften on extremely long inputs as the model's attention spreads thin ("lost in the middle"). Choose it when the document reliably fits the window with margin and you value coherence and simplicity — the common case for single contracts, papers, reports, and meeting transcripts.
Map-reduce (summarize chunks, then summarize the summaries)
You split the document into chunks, summarize each chunk independently (the "map" step — embarrassingly parallel), then summarize the collection of chunk summaries into one final summary (the "reduce" step), in multiple rounds if there are very many chunks. Pros: scales to any document length — a 1,000-page document is just more chunks; the map step parallelizes, so it is fast and is the natural fit for batch inference; and you can run the cheap map step on a small model and reserve a slightly better model only for the reduce step. Cons: more moving parts; the final summary can lose global coherence or cross-chunk connections (a fact in chunk 2 that recontextualizes chunk 40) because no single call ever saw the whole document; and chunk-boundary effects need care. Choose it when documents exceed the window, vary wildly in length, or when you are summarizing at scale and want to control cost by running the bulk of the work on a cheap model and on batch.
A useful third pattern — refine (iterative)
Between the two sits the refine pattern: summarize the first chunk, then for each subsequent chunk ask the model to update the running summary with the new information. It preserves more cross-chunk coherence than naive map-reduce because the summary carries forward, but it is inherently sequential (no parallelism, so slower and not batch-friendly) and an early error can propagate. Refine is a good middle ground for moderately long documents where coherence matters more than throughput; map-reduce wins when throughput and scale dominate.
Default to single-pass when the document fits the context window with margin — it is simpler and more coherent. Switch to map-reduce when documents exceed the window, vary wildly in length, or you are summarizing in bulk and want the cheap-model + batch cost profile. Use refine when coherence matters but the document is too long for one pass and throughput is not the priority. Many production systems route by document length: short → single-pass, long → map-reduce.
IVChoosing a model — and why summarization rarely needs a frontier model
Summarization is one of the easiest tasks for a modern language model, which has a happy consequence: you almost never need the most expensive model. The right discipline is to pick the cheapest tier that clears your quality bar — and because summaries are input-token-heavy, that choice swings the bill enormously.
On Bedrock the relevant tiers for summarization run from very cheap, very fast small models — Amazon Nova Micro and Nova Lite, Claude Haiku, small Llama and Mistral models — up through mid-tier models (Nova Pro, Claude Sonnet) and frontier models reserved for the hardest reasoning. For the large majority of summarization — meeting notes, support-ticket digests, article and report summaries, contract overviews — a small tier produces summaries that are indistinguishable from a frontier model's to most readers. Spend the model budget only where the task is genuinely hard: dense legal or financial reasoning, multi-document synthesis, or summaries that must extract subtle implications rather than restate the obvious.
Two structural facts make model choice the dominant cost lever for summarization specifically. First, summarization is input-heavy: you pay to push the entire (long) document in and only get a short summary out, so the input-token rate matters far more than the output-token rate — exactly the rate a cheaper model slashes. Second, in map-reduce the cheap map step dominates the token count, so running map on a small model and (optionally) reduce on a mid-tier model captures almost all the savings while keeping the synthesis sharp. The per-token rate difference, multiplied by document-scale input volume, is routinely the difference between a $50 and a $5,000 monthly bill for identical throughput.
A practical selection method: assemble 20–50 representative documents with reference summaries (human-written or human-approved), run two or three candidate models, and score them on faithfulness and completeness (section VI). Promote the cheapest model that clears your bar. Re-run the bake-off when AWS ships new tiers — the cheap end of the catalog improves constantly, and a model that was borderline last quarter is often comfortably good enough now. See amazon-bedrock-pricing for the full per-model rate table.
| Tier | Example models | Relative cost | Good for | Watch-out |
|---|---|---|---|---|
| Small / fast | Nova Micro/Lite · Claude Haiku · small Llama/Mistral | Lowest | The bulk of summarization; map step; high volume | May miss subtle implications in dense docs |
| Mid-tier | Nova Pro · Claude Sonnet | Moderate | Harder synthesis; the reduce step; nuanced docs | Overkill (and pricey) for routine summaries |
| Frontier | Top Claude / Nova Premier-class | Highest | Dense legal/financial reasoning; multi-doc synthesis | Rarely needed for summarization; biggest bill |
| Long-context | Large-window Claude / Nova | Per-token + big input | Single-pass over very large documents | Cost scales with the whole document per call |
VPrompt patterns that produce faithful summaries
The same model produces a vague, generic summary or a sharp, faithful one depending almost entirely on the prompt. A handful of patterns do most of the work — and they are the same patterns whether you are summarizing one document or running a corpus through batch.
The through-line of every good summarization prompt is constrain the model to the source and tell it exactly what you want. Summarization is grounded by definition — the source is right there in the prompt — so the prompt's job is to stop the model embellishing, to fix the shape of the output, and to set the level of compression.
- Ground it explicitly: Instruct the model to summarize using only the provided document and to never add facts, figures, or claims that are not in the source. A single line — "Summarize only what the document states; do not add outside information or speculation" — measurably cuts embellishment.
- Specify length and format: Vague prompts produce vague summaries. Say how long ("a 150-word executive summary," "5–8 bullet points," "one paragraph per section") and in what structure (executive summary + key points + action items/risks). A fixed schema also makes summaries comparable and machine-parseable downstream.
- Specify the audience and purpose: A summary for an executive differs from one for an engineer or a lawyer. Tell the model who it is for and what decision it supports ("summarize this contract for a non-lawyer founder deciding whether to sign; foreground obligations, termination, and liability"). Purpose focuses the compression.
- Ask for extraction where structure exists: For contracts, filings, and forms, an extract-then-summarize prompt ("list the parties, term, payment terms, termination clauses, and liability caps, then write a short narrative summary") is more faithful than a free-form summary, because you force the model to ground specific fields before prose.
- Request grounded citations or quotes: Asking the model to attach the supporting sentence or section to each key point (where your output format allows) both improves faithfulness and lets a human verify. Even "note the section each point came from" raises grounding.
- Write a strong reduce prompt (map-reduce): The reduce step is where map-reduce summaries go disjointed. Instruct the model to synthesize the chunk summaries into one coherent summary, de-duplicate overlapping points, resolve contradictions toward the source, and preserve the overall structure — not to concatenate.
- Handle "nothing to summarize" gracefully: For empty, boilerplate, or off-topic inputs, tell the model to say so rather than invent substance. This matters most in batch, where a bad input should produce an honest "no substantive content" rather than a fabricated summary that pollutes the dataset.
If you add one instruction to a summarization prompt, make it the grounding constraint: "Summarize only what the document states — do not add information, figures, or conclusions that are not in the source; if the document does not cover something, omit it." Pair it with an explicit length and a fixed output structure and most faithfulness and consistency problems disappear before you ever change models.
VIEvaluating summaries — faithfulness, completeness, and relevance
"The summary reads well" is not evaluation. Summaries fail in three distinct ways — they invent things, they miss things, or they drift off-purpose — and you need metrics that isolate each so you know whether to fix the prompt, the model, or the chunking.
Build a fixed evaluation set first: 30–200 representative documents, each paired with a reference summary (human-written, or human-approved good output) and ideally a checklist of the key points a correct summary must contain. Run it on every change — a new model, a new chunk size, a tweaked prompt — so you can tell whether the change actually helped instead of guessing. The three metrics below are the core of summarization evaluation, and an LLM-as-a-judge on Bedrock can score most of them automatically.
- Faithfulness (groundedness): Does every claim in the summary follow from the source, or did the model add unsupported facts, figures, or conclusions? This is the anti-hallucination metric and the most important one for summarization — a confident, well-written but wrong summary is the worst failure mode. Low faithfulness is usually a prompt problem (tighten the grounding constraint) or, occasionally, a too-weak model.
- Completeness (coverage): Did the summary capture the key points, or did it drop something that mattered? Score it against your per-document checklist of must-include points. Low completeness on long documents often means a chunking or map-reduce problem — a key point lived in a chunk that got under-weighted in the reduce step — more than a model problem.
- Relevance / focus: Does the summary serve its stated purpose and audience, or is it a generic restatement? A summary can be faithful and complete yet useless because it foregrounds the wrong things. Fix with a sharper purpose/audience instruction in the prompt.
- Conciseness & consistency: Is it as short as it should be without losing substance, and does it follow the required format every time? Inconsistent length or structure across documents is a prompt/format problem and matters when summaries feed a downstream UI or dataset.
How to run it on AWS
Amazon Bedrock includes model evaluation with an LLM-as-a-judge option: you supply your dataset of inputs (and reference summaries), and Bedrock scores response quality — including faithfulness/groundedness and relevance — so you can compare models and configurations on the same set and pick a winner objectively. For DIY pipelines, the same metrics are available in open-source evaluation frameworks that run anywhere. Either way the discipline is identical: a fixed golden set, automated scoring, and a number that moves when you change a knob.
Two non-negotiables for production. Log every summarization — source reference, prompt, model, and output — so any summary can be reproduced and audited. And keep a human-review sample: automated judges are good at catching invention and drift but miss domain-specific errors (a subtly mis-stated legal obligation, a flipped financial sign) that a subject-matter expert catches instantly. For high-stakes documents, a human-in-the-loop approval step before a summary is acted on is the right default.
VIISummarizing a whole corpus — batch inference and the bulk pattern
Summarizing one document on demand is an API call. Summarizing a backlog of a million documents — or re-summarizing a corpus when you change models — is a data-engineering job, and the right tool for it on Bedrock is batch inference, which halves the bill for work nobody is waiting on in real time.
A huge share of summarization is not interactive: digesting an archive of contracts, pre-computing summaries for every article in a catalog, condensing a year of support tickets, or summarizing a whole research corpus. Nobody is staring at a spinner — you just need the whole job done by a deadline. That is the exact shape Amazon Bedrock batch inference is built for: you write your inputs as JSONL records to Amazon S3, submit one asynchronous job (CreateModelInvocationJob), and Bedrock processes them in the background and writes one summary per input back to S3 — at roughly 50% of the on-demand token rate for the same model and tokens. For corpus-scale summarization, this is the single easiest cost win, and it composes perfectly with map-reduce: the map step is embarrassingly parallel, so it slots straight into a batch job.
The bulk pattern, end to end: parse the corpus once (Bedrock Data Automation / Textract over the documents in S3), chunk where needed, write the per-chunk (or per-document) summarization requests as JSONL to S3, run a batch job on a right-sized small model, reconcile the outputs back to your documents by record id, and — for map-reduce — run a second pass (often a smaller batch or a few on-demand calls) for the reduce step. Because each record is independent, batch fits the map step exactly; the reduce step, which needs a document's chunk summaries together, runs as a follow-on. The whole thing slots beside Glue, Athena, Step Functions, or whatever orchestrates your data flow — a summarization job is just "transform this S3 dataset with a model."
The two cost levers multiply here, which is the whole point. Pick the cheapest model that clears the bar (the big swing, because summarization is input-heavy), then run it on batch (~50% off). For a large corpus the combined effect over a frontier-model-on-demand baseline is routinely an order of magnitude or more. Keep real-time summarization (a user pastes a document and waits) on the on-demand path — often with prompt caching if a long instruction or shared context repeats — and send the bulk backlog to batch. See amazon-bedrock-batch-inference for the full mechanics and the cost math.
Real-time summarization (a human is waiting) → on-demand, smallest adequate model, prompt caching if the instruction/context repeats. Bulk or corpus summarization (a deadline, not a person, is waiting) → batch inference (~50% off) on a right-sized small model, with map-reduce for long documents. The two cost levers — cheap model × batch — multiply, and at corpus scale that is the difference between a hobby-budget job and an enterprise bill.
VIIIThe summarization cost stack on AWS — where the money goes
A summarization bill has four line items. None is exotic, but together they surprise teams that budgeted only for "the model." Here is the full stack, the lever on each, and a worked example so you can reproduce the math for your own job.
The figures below are representative as of 2026 to show the shape of the bill, not a quote — always check the AWS pricing page for current rates. The dominant cost in almost every summarization workload is generation input tokens (you push whole documents in), which is exactly why model right-sizing and batch are the two biggest levers.
A worked example (bulk summarization)
The job: summarize 500,000 documents/month, each averaging 4,000 input tokens (a long report or contract) and producing a 300-token summary, on a small model (Amazon Nova Lite-class). Monthly volume: 500K × 4,000 = 2,000M (2B) input tokens and 500K × 300 = 150M output tokens.
On-demand, small model. At Nova Lite's representative rates of ~$0.06 / 1M input and ~$0.24 / 1M output: input = 2,000 × $0.06 = $120; output = 150 × $0.24 = $36 → ≈ $156/month. On batch (~50% off): ≈ $78/month — same summaries, same documents, run overnight.
Now compound it with model choice. The same job on a frontier Sonnet-class model (~$3 / $15 per 1M) would be 2,000 × $3 + 150 × $15 = $6,000 + $2,250 = ~$8,250/month on-demand, or ~$4,125 on batch. Identical throughput, ~50× the cost — almost entirely because of the input-token rate on a model the task did not need. The arithmetic teaches the lesson twice: right-size the model first (the big swing), then halve it with batch.
| Cost line | When you pay | Driver | Main lever to control it |
|---|---|---|---|
| Parsing | Per document processed | Pages / documents parsed (Textract / Data Automation) | Parse once and cache the clean text; skip OCR for born-digital text |
| Generation — input | Per summary (usually the largest) | Document length × model input rate | Cheapest adequate model; batch (~50% off); don't re-summarize unchanged docs |
| Generation — output | Per summary | Summary length × model output rate | Right-size summary length; usually small vs input |
| Evaluation | Per eval run | Judge-model calls × eval-set size | Fixed golden set; sample rather than score 100% of traffic |
IXWho can fund a summarization pipeline
Bedrock inference (on-demand and batch), parsing with Textract or Bedrock Data Automation, evaluation, S3 and orchestration all draw down AWS credits automatically.
Partner-filed funding: a vetted AWS partner can file POC (proof of concept) funding to test a summarization use case (often a first corpus backfill), or incremental workload (IW) funding if you already run on AWS and this is new work. For startups this is typically $50K to $150K. Depends on the workload; AWS approves.
AWS Activate: you apply yourself. Activate Founders gives self-funded startups $1,000 to start, up to $5,000. Activate Portfolio gives up to $200,000 with the Org ID from your VC, accelerator or angel group, if it is an Activate Provider. A consulting partner cannot file Activate for you.
CloudRoute matches you with one vetted AWS partner who can build the pipeline (parsing, chunking and map-reduce, prompts, batch jobs, evaluation) and check which funding fits. Startups pay CloudRoute nothing. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding.
Single-pass vs map-reduce vs refine — which summarization strategy to build
This is the comparison that decides your architecture. Read it as "default to single-pass when the document fits; move to map-reduce for length and scale; use refine when coherence beats throughput." Figures and limits are representative 2026 illustrations, not quotes.
| Dimension | Single-pass (long-context) | Map-reduce | Refine (iterative) |
|---|---|---|---|
| How it works | Whole document in one call | Summarize chunks → summarize summaries | Carry a running summary, update per chunk |
| Max document length | Capped by context window | Unlimited (just more chunks) | Unlimited (sequential) |
| Coherence of output | Highest — model sees everything | Can fragment at the reduce step | Good — summary carries forward |
| Parallelism / speed | One call | Map step is fully parallel (batch-friendly) | Sequential — slowest |
| Cost profile | Whole doc per call (pricey on big models) | Cheap model on the bulky map step | Per-chunk, sequential |
| Build complexity | Lowest | Highest (chunk + assemble) | Moderate |
| Best for | Documents that fit the window with margin | Very long docs; bulk/corpus at scale | Long docs where coherence > throughput |
How a match works (example)
- You tell us about the work
You fill in a short form: what you run today, what you want to move, test or build, your stage and your region.
- We pick one partner
We read your answers and pick one vetted AWS consulting partner that fits the work, the stack and the region. We reply within 24 hours; your matched partner reaches out within 5 business days.
- The partner checks POC funding
Your matched partner scopes the test with you. Then it checks whether the test qualifies for POC (proof of concept) funding. A POC can fit whether or not you run on AWS today.
- The partner files, AWS decides
If the work qualifies, the partner files the funding request with AWS. AWS reviews it and decides. The amount depends on the workload.
- You pay nothing
CloudRoute is free for startups. Partners pay CloudRoute a monthly membership plus 10% of AWS-approved funding.
AWS Activate credits are separate, and you apply for them yourself. Use Activate Founders if no investor backs you. Use Activate Portfolio with the Org ID or invite from your VC, accelerator, incubator or angel group if it is an Activate Provider. A consulting partner cannot apply for Activate on your behalf.
Common questions
How do you summarize documents with AI on AWS?
Should I use map-reduce or a long-context single-pass for summarization?
Which model should I use to summarize documents on Bedrock?
How do I summarize a large corpus of documents cheaply on AWS?
How do I parse PDFs and scanned documents before summarizing?
How do I stop an AI summary from hallucinating or inventing details?
How do you evaluate the quality of an AI-generated summary?
What does AI document summarization on AWS actually cost?
Can AWS funding cover building a summarization pipeline?
Build document summarization on AWS with a partner who can file the funding
A vetted AWS partner can design and ship the pipeline: parsing with Bedrock Data Automation or Textract, map-reduce for long documents, right-sized models, batch for corpora, and evaluation. The same partner checks whether the work qualifies for POC or IW funding: typically $50K to $150K for startups. Depends on the workload; AWS approves. CloudRoute is free for startups.